the bioanalytics group llc global optimization toolkit project first prototype delivery
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The BioAnalytics Group LLC
Global Optimization Toolkit Global Optimization Toolkit ProjectProject
First Prototype Delivery
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
OutlineOutline
Introductions
Purposes of the Toolkit
Example Workflow
Modules
Preliminary Results
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
Purpose of the ToolkitPurpose of the Toolkit
Primary: Provide Robust, Easy-to-use Global Optimization alternatives to local optimization packages provided in MATLAB.
Secondary: Provide supporting tools to use Global Optimization in biomodel parameter estimation projects.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
Why is a Toolkit Needed?Why is a Toolkit Needed?
Local optimization routines in MATLAB leave a lot of questions unanswered.
Are there other local minima that should be considered?
What is the confidence region of the parameters?
How good is the fit of the model to data?
How do I integrate data from multiple experiments?
Available global optimization packages for MATLAB are very basic or require considerable trial-and-error and experience to use.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
Why Global Optimization?Why Global Optimization?
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
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Problem StatementProblem Statement
Minimize f(p), subject to bounds constraints on the vector p of parameters. (lb < p < ub)
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
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Parameter EstimationParameter Estimation
Special case of optimizationf is a function of the error (ŷ-y) between simulated data ŷ and experimental measurements y, especially time-series data.
Special-Special Case:ŷ is the solution to an initial value problem.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
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Example WorkflowExample Workflow
1. Design a model in MATLAB.
2. Pick the parameters to be estimated.
3. Select a fitting function.
4. Import the experimental data into MATLAB.
5. Optimize the parameters to fit the data.
6. View the results.
7. Estimate confidence intervals.
8. Report results.
9. Get more data, change model, re-estimate, etc.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
ModulesModules
Algorithm Selection
Data Import
Parameter Selection
Multiple Experiments
Postprocessing and Visualization
2003-2004 The BioAnalytics Group LLC. All rights reserved.
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AlgorithmsAlgorithms
Adaptive Simulated Annealing
Branch-and-Fit
Differential Evolution
Evolutionary Strategy (+Stochastic Ranking)
2003-2004 The BioAnalytics Group LLC. All rights reserved.
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Data FormatData Format
Most Common Internal FormatT,Y, E
T is a vector of Nt measurement times
Y is a matrix of Nt-by-Nm measurements
E is an optional matrix of Nt-by-Nm measurement errors.
Easy to import from text files, Excel, etc. using MATLAB provided functions.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
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Parameter SelectionParameter Selection
Most common internal parameter format:P,C, F
P is a vector of Np parameter estimates
C is a matrix of Np-by-Np covariance (often diagonal)
F is an optional vector of Nf parameters to be estimated.P=[p1, p2, p3, p4, p5, p6, p7, p8, p9, p10]T
F=[1, 8, 10, 2] (I want to fit only first, eighth, tenth, second parameters)
Easy to import from text, Excel, MATLAB files with MATLAB functions.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
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Multiple ExperimentsMultiple Experiments
Problem:One ModelDifferent data setsDifferent parameters to be fit for each data set.
Local and Global ParametersLocal parameters take different (optimal) values for each data set.Global parameters have one optimal value for all data sets.
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
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Multiple ExperimentsMultiple Experiments
Batch EstimationRun all data sets in a single cost function finding one optimal set of parameters.
Option: Local parameters for each experiment
Sequential EstimationRun each data set in sequence, improving the parameter estimate with each new data set.
Find the best global parameter values
Find the best local parameter values
Toolkit Implements a Bayesian sequential estimator
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
Postprocessing: View resultsPostprocessing: View results
2003-2004 The BioAnalytics Group LLC. All rights reserved.
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Postprocessing: Confidence Postprocessing: Confidence IntervalsIntervals
Parameter Confidence Regions
2003-2004 The BioAnalytics Group LLC. All rights reserved.
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Preliminary ResultsPreliminary Results
3 Algorithms all competitive on Novartis PERSIMrunCHIAKI test case.
Differential Evolution (With TBAG modifications)
Evolutionary Strategies
Adaptive Simulated Annealing
One algorithm not competitive: Branch-and-fit
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
2003-2004 The BioAnalytics Group LLC. All rights reserved.
The BioAnalytics Group
Confidential
To Be DoneTo Be Done
GUI completion
Benchmarks
Support, Feedback and Updates
External Interface: acslXtreme
Follow-up work